In distributed systems, change is inevitable - but breaking things doesn’t have to be. As your system evolves, so will the data exchanged between services. In Kafka-based architectures, that evolution must be handled carefully. Fail to do so, and you'll end up with incompatible consumers, corrupted data, and replay nightmares.
This article dives into schema evolution in Kafka, with a sharp focus on using Protobuf and Go to build resilient contracts. We'll cover versioning strategies, evolution-safe patterns, common pitfalls, and practical examples using kafka-go and a schema registry (Buf or Confluent).
When your services communicate through Kafka topics, they rely on a shared contract - the event schema. But these schemas aren’t static. You’ll inevitably need to:
Add new fields (e.g., tax…